Anna Bombak, Chuck Humphrey, Lindsay Johnston and Leah Vanderjagt

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The Winter Institute on Statistical Literacy for
Librarians

• Demystifying statistics for the practitioner

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Outline

• Introductions
• Statistics and data what are we talking about?
• Definitions, standards and metadata
• Official statistics national
• Official statistics international
• Census geography and small area statistics
• Non-official statistics

Day 1
Day 2
Day 3
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Introductions your backgrounds

• Please introduce yourself
• Your name
• Your institutional affiliation
• Your librarian responsibilities
• Is there anything in particular that you are
  hoping in covered this workshop?

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Introductions your backgrounds

• You are equally split between non-academic and
  academic libraries.
• The largest group, with 13, is from universities
  other than the U of A.
• The second largest group, with 10, is from
  government libraries.

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Introductions your backgrounds

• Geographically, 21 of you are from Alberta and
  nine are from other provinces.
• We have representa-tion from Ontario, Manitoba,
  Saskatch-ewan and Alberta.
• Thirteen are from the Edmonton region.

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Statistics what are we talking about
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Statistics are ubiquitous

• Statistics are generated today about nearly
  every activity on the planet. Never before have
  we had so much statistical information about the
  world in which we live. Why is this type of
  information so abundant? For one thing,
  statistics have become a form of currency in
  todays information society. Through computing
  technology, society has become very proficient in
  calculating statistics from the vast quantities
  of data that are collected. As a result, our
  lives involve daily transactions revolving around
  some use of statistical information.

Data Basics, page 1.1
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Numeric information

• Statistics
• numeric facts/figures
• created from data, i.e, already processed
• presentation-ready
• Data
• numeric files created and organized for
  analysis/processing
• requires processing
• not display-ready

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Numeric information
Geography Region Time Periods Unit of
Observation Attributes Smokers Education Age S
ex
The cells in the table are the number
of estimated smokers.
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Statistics are about definitions!
Definitions Sex Total Male Female Periods 1994
-1995 1996-1997
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Statistics are about definitions!
Some definitions are based on standards while
others are based on convention or practice. For
example, Standard Geography classifications
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Numeric information
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Stories are told through statistics

• The National Population Survey in the previous
  example had over 80,000 respondents in 1996-97
  sample and the Canadian Community Health Survey
  in 2005 has over 130,000 cases. How do we tell
  the stories about each of these respondents?
• We create summaries of these life experiences
  using statistics.

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Summary

• Statistics are derived from observational,
  experimental or simulated data .
• A table is a format for displaying statistics and
  presents a summary or one view of the data.
• Tables are structured around geography, time and
  attributes of the unit of observation.
• Statistics are dependent on definitions.
• Statistics summarize individual stories into
  common or general stories.

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Methods producing data
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Methods producing data

• A particular discipline or field will tend to be
  dominated by one of these three methods, although
  outputs may also exist from the other two
  methods.
• Consequently, the knowledge disseminated within a
  field is often fairly homogeneous in how
  statistical information is used and reported.
• Knowing this and the life cycle in which
  statistics are produced can help in the search
  for statistics.

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Life cycle of survey statistics
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Life cycle of survey statistics
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Life cycle applied to health statistics
Health Information Roadmap Initiative
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Life cycle applied to health statistics
Health Information Roadmap Initiative
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Reconstructing statistics

• One way to see the relationship between
  statistics and the data upon which they were
  derived is to reconstruct statistics that someone
  else has produced from data that are publicly
  accessible.

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Reconstructing statistics
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Health Information Roadmap Initiative
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Reconstructing statistics

• The statistics that we will reconstruct are
  reported in Health Facts from the 1994 National
  Population Health Survey, Canadian Social
  Trends, Spring 1996, pp. 24-27.
• The steps we will follow are
• identify the characteristics of the respondents
  in the article
• identify the data source
• locate these characteristics in the data
  documentation
• find the original questions used to collect the
  data
• retrieve the data and
• run an analysis to reproduce the statistics.

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The findings to be replicated

• Page 26

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Summary of variables identified

• Findings apply to Canadian adults
• Likely need age of respondents
• Men and women
• Look for the sex of respondents
• Type of drinkers
• Look for frequency of drinking or a variable
  categorizing types of drinkers
• Age
• Look for actual age or age in categories
• Smokers
• Look for smoking status

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Identify the data source

• Survey title is identified National Population
  Health Survey, 1994-95
• Public-use microdata file is announced
• Page 25 of the article

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Locate the variables

• Examine the data documentation for the National
  Population Health Survey, 1994-95
• PDF version is on-line
• Use TOC and link to Data Dictionary for Health
• Identify the variables from their content
• NOTE check how missing data were handled
• Trace the variables back the questionnaire
• Did sampling method require weighting cases?
• NOTE in addition to the other variables, is a
  weight variable needed to adjust for the sampling
  method?

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Retrieve and analyze the data

• For universities subscribed to the Statistics
  Canada Data Liberation Initiative (DLI), the
  public use microdata from the NPHS can be
  downloaded without additional cost. See the
  Statistics Canada Online Catalogue for further
  cost details.
• Make use of local data services to retrieve data
  from the NPHS.

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Lessons from the NPHS example

• This example demonstrates the distinction between
  creating statistics and interpreting statistics
  that have been created by others.
• This is an important distinction because
• Choices are made in creating statistics.
• Interpreting statistics requires an ability to
  understand the choices that were made.
• Searching for statistics that others have created
  can be facilitated by understanding these points.

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Provide a different perspective

• Building on the previous example using the NPHS,
  compare the statistics from an article about
  young adults giving and receiving help to their
  parents age cohort.

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Statistics are about definitions
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Statistics are about definitions
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Statistics are about definitions

• Look at the Census definitions
• Definitions are in the Census Handbook (2001) and
  the Census Dictionary (2006)
• Search by Census Variable under Topic-Based
  Tabulations (2006) for value categorizations
• Look at some standard classifications used in
  statistics
• SIC, NAICS, NOC, Standard Classification of Goods
  (SCG), Standard Geographic Classification (SGC),
  Classification of Instructional Programs (CIP),
  ICD10

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Statistics in the News

• Three recent newspaper articles that include
  statistics in them have been selected for this
  exercise. For each of the articles, answer the
  following questions.
• What is the concept represented by the statistic
  or statistics in this story?
• Is a definition for this concept provided? If it
  is, what is it? Or is the definition implicit?
• Are any classifications identifiable? What are
  they?
• Are the data from which this statistic was
  derived identified in the article?

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Metadata for describing tables

• As we have discussed, tables are a typical
  display format for statistics. Because tables
  are often published within an article, they dont
  get indexed. Therefore, to find published tables
  requires a connection between characteristics in
  the table with other indexed content.
• Two indices of tables that exist are Statistical
  Universe and Tablebase. They use traditional
  elements to index tables without defining unique
  properties of tables.

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Metadata for describing tables

• What are the properties of a table that we might
  use to develop useful descriptors for describing
  their content?
• What is the motivation for doing this exercise?
• Searching for tables that were indexed using such
  descriptors would allow finding statistics much
  easier.
• The movement toward open access journals and
  publishing lends an opportunity to introduce
  metadata elements for statistical tables.
• Once we have statistical tables described more
  comprehensively, opportunities will exist to link
  tables to the data sources from which the
  statistics in the table were derived.

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Title
Variables Average Tuition Discipline Academic
Year Province
Statistical Metric Dollars
Footnote
Producer
Date
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What are the metadata characteristics of tables
graphs?

• Is a title provided?
• Is an author, producer or agency identifiable?
• Is there a date of creation or publication?
• What is the entity that has been observed to make
  this statistic? That is, what is the unit of
  observation?
• Are the characteristics of the unit observation
  (i.e., variables) and their categories clearly
  identified and defined?
• Is there a key to explain the use of colours or
  lines in the graph?

• Is the type of statistic clearly identified?
  That is, does the table or graph contain
  percentages, counts, averages, etc.?
• Is there a scale for the numbers presented in the
  table or graph?
• Is there an overall figure or number (N)
  presented upon which the table or graph was
  calculated?
• Are there footnotes?
• Are geography, time and social content clearly
  expressed in the table or graph?

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Summary

• If statistical tables and graphs were described
  and indexed by rich metadata, our ability to
  locate statistics would be greatly enhanced.
• In the absence of such metadata, we use elements
  of this metadata structure to search our existing
  databases.
• The next generation of metadata in the field of
  data will work to integrate the description of
  both data and statistics.